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- PhD research experience prior to anticipated start date. Academic excellence, potential to bring new ideas and approaches to Princeton University and to interact successfully with a broad range of
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials
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the development and testing of new materials. The work will involve reactor design and setup with gas flow capability and process optimization. Qualified candidates should have a Ph.D. in chemistry, physics
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polymer physics. The successful candidate will develop strategies to design, synthesize, and characterize the properties of soft materials using advanced microscopy techniques and related methods
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, with emphasis on HRXPS, LEIS, AES, LEED, TPD, HREELS, Raman scattering, and SEM. Qualified candidates should possess experience for the design, construction, operation, and maintenance of UHV instruments
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and a strong commitment to excellence in education are encouraged to apply. PhD is expected by the start date. Applicants must apply online at https://puwebp.princeton.edu/AcadHire/position/38042 and
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of reinforcement learning in the brain and leverage these models to assist the experimental groups with experimental design and multimodal data analysis of neurophysiological, causal, and behavioral data
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to excellence in education are encouraged to apply. A PhD in Materials Science, Optics, Physics, Chemistry, Electrical, Chemical, Mechanical, Civil or Bio Engineering or related area is required. We
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senior researcher in the areas of soft materials and polymer physics. The successful candidate will develop strategies to design, synthesize, and characterize the properties of soft materials using
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The Rosen Research Group at Princeton University (https://rosen.cbe.princeton.edu) is searching for a postdoctoral or more senior researcher interested in computational materials design and